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< title > LCOV - coverage.info - bayesnet/ensembles/AODELd.cc< / title >
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< tr > < td class = "title" > LCOV - code coverage report< / td > < / tr >
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< td width = "10%" class = "headerItem" > Current view:< / td >
< td width = "10%" class = "headerValue" > < a href = "../../index.html" > top level< / a > - < a href = "index.html" > bayesnet/ensembles< / a > - AODELd.cc< span style = "font-size: 80%;" > (source / < a href = "AODELd.cc.func-c.html" > functions< / a > )< / span > < / td >
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< td width = "5%" class = "headerCovTableHead" > Coverage< / td >
< td width = "5%" class = "headerCovTableHead" title = "Covered + Uncovered code" > Total< / td >
< td width = "5%" class = "headerCovTableHead" title = "Exercised code only" > Hit< / td >
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< td class = "headerItem" > Test:< / td >
< td class = "headerValue" > coverage.info< / td >
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< td class = "headerItem" > Lines:< / td >
< td class = "headerCovTableEntryHi" > 100.0 %< / td >
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< td class = "headerItem" > Test Date:< / td >
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< td class = "headerValue" > 2024-04-30 13:59:18< / td >
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< td class = "headerItem" > Functions:< / td >
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< pre class = "sourceHeading" > Line data Source code< / pre >
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< span id = "L1" > < span class = "lineNum" > 1< / span > : // ***************************************************************< / span >
< span id = "L2" > < span class = "lineNum" > 2< / span > : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez< / span >
< span id = "L3" > < span class = "lineNum" > 3< / span > : // SPDX-FileType: SOURCE< / span >
< span id = "L4" > < span class = "lineNum" > 4< / span > : // SPDX-License-Identifier: MIT< / span >
< span id = "L5" > < span class = "lineNum" > 5< / span > : // ***************************************************************< / span >
< span id = "L6" > < span class = "lineNum" > 6< / span > : < / span >
< span id = "L7" > < span class = "lineNum" > 7< / span > : #include " AODELd.h" < / span >
< span id = "L8" > < span class = "lineNum" > 8< / span > : < / span >
< span id = "L9" > < span class = "lineNum" > 9< / span > : namespace bayesnet {< / span >
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< span id = "L10" > < span class = "lineNum" > 10< / span > < span class = "tlaGNC tlaBgGNC" > 102 : AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)< / span > < / span >
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< span id = "L11" > < span class = "lineNum" > 11< / span > : {< / span >
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< span id = "L12" > < span class = "lineNum" > 12< / span > < span class = "tlaGNC" > 102 : }< / span > < / span >
< span id = "L13" > < span class = "lineNum" > 13< / span > < span class = "tlaGNC" > 30 : AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector< std::string> & features_, const std::string& className_, map< std::string, std::vector< int> > & states_)< / span > < / span >
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< span id = "L14" > < span class = "lineNum" > 14< / span > : {< / span >
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< span id = "L15" > < span class = "lineNum" > 15< / span > < span class = "tlaGNC" > 30 : checkInput(X_, y_);< / span > < / span >
< span id = "L16" > < span class = "lineNum" > 16< / span > < span class = "tlaGNC" > 30 : features = features_;< / span > < / span >
< span id = "L17" > < span class = "lineNum" > 17< / span > < span class = "tlaGNC" > 30 : className = className_;< / span > < / span >
< span id = "L18" > < span class = "lineNum" > 18< / span > < span class = "tlaGNC" > 30 : Xf = X_;< / span > < / span >
< span id = "L19" > < span class = "lineNum" > 19< / span > < span class = "tlaGNC" > 30 : y = y_;< / span > < / span >
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< span id = "L20" > < span class = "lineNum" > 20< / span > : // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y< / span >
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< span id = "L21" > < span class = "lineNum" > 21< / span > < span class = "tlaGNC" > 30 : states = fit_local_discretization(y);< / span > < / span >
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< span id = "L22" > < span class = "lineNum" > 22< / span > : // We have discretized the input data< / span >
< span id = "L23" > < span class = "lineNum" > 23< / span > : // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network< / span >
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< span id = "L24" > < span class = "lineNum" > 24< / span > < span class = "tlaGNC" > 30 : Ensemble::fit(dataset, features, className, states);< / span > < / span >
< span id = "L25" > < span class = "lineNum" > 25< / span > < span class = "tlaGNC" > 30 : return *this;< / span > < / span >
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< span id = "L26" > < span class = "lineNum" > 26< / span > : < / span >
< span id = "L27" > < span class = "lineNum" > 27< / span > : }< / span >
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< span id = "L28" > < span class = "lineNum" > 28< / span > < span class = "tlaGNC" > 30 : void AODELd::buildModel(const torch::Tensor& weights)< / span > < / span >
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< span id = "L29" > < span class = "lineNum" > 29< / span > : {< / span >
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< span id = "L30" > < span class = "lineNum" > 30< / span > < span class = "tlaGNC" > 30 : models.clear();< / span > < / span >
< span id = "L31" > < span class = "lineNum" > 31< / span > < span class = "tlaGNC" > 252 : for (int i = 0; i < features.size(); ++i) {< / span > < / span >
< span id = "L32" > < span class = "lineNum" > 32< / span > < span class = "tlaGNC" > 222 : models.push_back(std::make_unique< SPODELd> (i));< / span > < / span >
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< span id = "L33" > < span class = "lineNum" > 33< / span > : }< / span >
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< span id = "L34" > < span class = "lineNum" > 34< / span > < span class = "tlaGNC" > 30 : n_models = models.size();< / span > < / span >
< span id = "L35" > < span class = "lineNum" > 35< / span > < span class = "tlaGNC" > 30 : significanceModels = std::vector< double> (n_models, 1.0);< / span > < / span >
< span id = "L36" > < span class = "lineNum" > 36< / span > < span class = "tlaGNC" > 30 : }< / span > < / span >
< span id = "L37" > < span class = "lineNum" > 37< / span > < span class = "tlaGNC" > 30 : void AODELd::trainModel(const torch::Tensor& weights)< / span > < / span >
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< span id = "L38" > < span class = "lineNum" > 38< / span > : {< / span >
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< span id = "L39" > < span class = "lineNum" > 39< / span > < span class = "tlaGNC" > 252 : for (const auto& model : models) {< / span > < / span >
< span id = "L40" > < span class = "lineNum" > 40< / span > < span class = "tlaGNC" > 222 : model-> fit(Xf, y, features, className, states);< / span > < / span >
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< span id = "L41" > < span class = "lineNum" > 41< / span > : }< / span >
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< span id = "L42" > < span class = "lineNum" > 42< / span > < span class = "tlaGNC" > 30 : }< / span > < / span >
< span id = "L43" > < span class = "lineNum" > 43< / span > < span class = "tlaGNC" > 6 : std::vector< std::string> AODELd::graph(const std::string& name) const< / span > < / span >
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< span id = "L44" > < span class = "lineNum" > 44< / span > : {< / span >
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< span id = "L45" > < span class = "lineNum" > 45< / span > < span class = "tlaGNC" > 6 : return Ensemble::graph(name);< / span > < / span >
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< span id = "L46" > < span class = "lineNum" > 46< / span > : }< / span >
< span id = "L47" > < span class = "lineNum" > 47< / span > : }< / span >
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< tr > < td class = "versionInfo" > Generated by: < a href = "https://github.com//linux-test-project/lcov" target = "_parent" > LCOV version 2.0-1< / a > < / td > < / tr >
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